Headmen, shamans, and mothers: natural and sexual selection for computational services
Computer engineers face a dilemma. They must build systems with sufficient resources to solve the most complex problems the systems are expected to solve, but the systems will only need to solve such problems intermittently, resulting in inefficient use of expensive computational resources. This dilemma is commonly resolved with timesharing, networking, multitasking, and other technologies that enable computational resources to be shared with multiple users. The human brain, which evolved to acquire, store, and process information to make beneficial decisions, is likewise energetically expensive to build and maintain yet plausibly has idle capacity much of the time. We propose that humans evolved to use advantages in information or computational resources to provide computational services to others via a language-based “network” in exchange for payments of various sorts that helped subsidize the energetic costs of the brain. Specifically, we argue that with the Pleistocene transition of Homo to a niche in open habitats with a more meat-based diet, four major selection pressures for knowledge specialists began to act on the human lineage: (1) the need to resolve conflicts and maintain cooperation in larger multilevel societies, which lead to the rise of knowledge-based leaders as decision-making and conflict resolution specialists who were “paid” with increased mating success or resources; (2) the need for greater defense against zoonotic pathogens, which lead to the rise of shamans as medical knowledge specialists, who were “paid” with increased mating success or resources; (3) the greater complexity of mothering with shorter interbirth intervals and longer periods of juvenile dependency, which led to mothers as both decision-making and medical specialists, who were “paid” with increased inclusive fitness; and (4) the need to make more efficient use of an increasingly large and energetically expensive brain.
1 Introduction
The computational complexity of a problem is measured in time complexity and space complexity, the number of steps and amount of memory required to solve it, respectively. Computational resources represent the capacity to solve problems of a given complexity (Arora & Barak, 2009; Dasgupta & Gershman, 2021). Substantial computational resources are expensive to build and maintain, yet their use can be inefficient when a user only needs to solve complex problems intermittently. Time-sharing and multitasking systems, developed to allow multiple users to interact concurrently with a single multimillion-dollar mainframe computer, are one way to increase efficiency (Corbató, Merwin-Daggett, & Daley, 1962). A second, proposed by Joseph Licklider, an employee at the United States Department of Defense Advanced Research Projects Agency (ARPA), was what he wryly termed an “Intergalactic Computer Network” enabling researchers to access computational resources in remote locations (Licklider, 1963). His proposal launched development of ARPANET, the predecessor of the internet (Lukasik, 2010).
The invention of computer time-sharing, multitasking, and network technologies allowed multiple users to make much more efficient use of expensive computational resources. These technologies, combined with the ubiquity of the internet, have given rise to a wide array of cloud computing services, accessible with application programming interfaces (APIs). In one important class of services, customers upload data, such as images or queries, to a company that subjects it to complex computational processing, such as image recognition or database retrieval, and returns the output for a small fee. Computational service providers often develop the software and hardware for internal use as part of their primary business but then open up the service to others to help pay for the high costs of building and maintaining the computational infrastructure (Armbrust et al., 2010; Mell & Grance, 2011; Miller, 2016).
There are important analogies between expensive computer systems and the brain. First, nervous systems evolved to map information to actions that, on average, increased fitness; that is, they evolved to make “good” decisions (Hagen et al., 2012). As we explain later, some good decisions require extraordinary computational resources. Second, nervous systems are expensive: human brain development takes over 15 years (Blakemore, 2012), and the total glucose used from birth through age 15 is equivalent to nearly half the total energy used for resting metabolism over this period (Kuzawa et al., 2014). See Figure 1. In adulthood, the brain continues to consume about 20% of basal energy (Herculano-Houzel, 2012).
Remarkably, brain metabolism is essentially fixed: the additional energy consumption associated with transitory cognitive demands might be less than 5% of the baseline energy budget (Raichle, 2006, 2015). Even when asleep, brain energy consumption during the REM cycle is the same as when awake, and during the non-REM cycle only reduces to about 85% of the waking value (DiNuzzo & Nedergaard, 2017). In short, human brain development is lengthy and extraordinarily energetically expensive, and operating the brain across adulthood requires a large, fixed energy cost.
1.1 Computational services
We draw an explicit analogy between the efficient use of expensive computing resources via electronic networks and the efficient use of energetically expensive nervous systems via a language-based “network.” Human foragers, relative to great apes, afford their expanded energy costs by increasing their rate of energy acquisition (Kraft et al., 2021), leaving more time for activities such as childcare, socializing, manufacturing, innovation, exploration, defense, and importantly, collecting, processing, and disseminating information that might be valuable to others. We argue that throughout human evolution when individuals were not using their cognitive resources to make decisions for themselves, they could subsidize the substantial cost of building and maintaining a large brain by offering some of these valuable computational services to others via language in return for a “payment”. The “payment” could have been any of the forms of social interactions that increased inclusive fitness, such as helping kin or long-term social partners, or receiving resources. Providing useful information, for instance, inspires epistemic gratitude, a positive emotional response that increases the likelihood of future reciprocation (Karabegovic, Wang, Boyer, & Mercier, 2024). In some cases the payoff was mating success, leading to sexual selection for computational abilities, a key element of our argument (briefly sketched in previous publications, Garfield, Hubbard, & Hagen, 2019; Garfield, Rueden, & Hagen, 2019).
Predator alarm calls are a paradigmatic example of a computational service in non-human animals. Detecting a predator is an extremely computationally demanding task, involving rapid processing of high bandwidth visual, auditory and olfactory data channels (Pereira & Moita, 2016). An alarm call is a computational service whose “payment” is the survival of kin, i.e., increased inclusive fitness (Price et al., 2015; Seyfarth, Cheney, & Marler, 1980).
Teaching is another important type of computational service (Castro & Toro, 2014). In social learning models, agents individually learn about environmental variation, such as toxic foods to avoid during pregnancy (Henrich & Henrich, 2010; Placek, Madhivanan, & Hagen, 2017), information that they can then transmit to others (Boyd & Richerson, 1985; Rogers, 1988). Providing information on, e.g., toxic plants or tool manufacture is valuable to most members of the population, and the information is typically valid for multiple generations. This computational service, which can be “paid” for via increased prestige and deference (Henrich & Gil-White, 2001), plays a central role in cultural evolution.
Computational services extend far beyond facilitating cultural transmission, however, because much transmitted information is only useful for specific individuals at specific points in time, and this information is therefore not fodder for cultural evolution. Communicating useful information on transient environmental conditions, for instance, such as “that tree has ripe fruit”, is a computational service that was probably one of the major selection pressures for the evolution of language (Pinker & Jackendoff, 2005), but this information is not fodder for cultural evolution. There are also universal cognitive mechanisms (Barkow, Cosmides, & Tooby, 1992; Barrett, 2014; Boyer, 2018), but some individuals have greater memory and processing due to, e.g., genetic variation, or have greater time to master a knowledge domain (Ericsson & Charness, 1994) due to, e.g., fewer allocare responsibilities, and can therefore provide better and/or faster solutions to common problems, such as estimating the quantity of resources in a patch or the best route from one point to another, as a service to others. In one model of decision making, there is a tradeoff between reward and cognitive cost, such that more complex decision rules yield greater rewards, but incur greater cognitive costs (Lai & Gershman, 2024). Individuals with greater computational resources could therefore identify decision options yielding greater rewards.
Other valuable cognitive services employ rare or proprietary information. The number of dyadic relationships in a group of size \(n\) grows as \(n^2\), for example, so keeping track of all relationships in a group of even moderate size is computationally challenging. Individuals with accurate information about these dyadic relationships could use it to help resolve disputes (or to manipulate others to benefit themselves). Yet other computational services involve complex weighting of factors that are specific to one individual at a single point in time. Conflict resolution, for instance, is subject to culturally evolved rules that typically apply to everyone. Nevertheless, resolving a particular conflict within these constraints can be difficult due to limited information and the need to weigh many factors. Leaders could offer valuable advice and counsel on resolving particular conflicts by drawing on their proprietary knowledge of the interests of the parties involved, their preferences, personalities, and past histories, along with potential bargaining chips. They could also draw on their individually learned heuristics of conflict resolution. But advice to one person would not necessarily be applicable to any other person, nor even to that same person in the future, and hence is not fodder for cumulative cultural evolution.
It would be difficult and perhaps impossible to reverse engineer many of these computational services by simply observing limited instances of their delivery, a task whose challenges are analogous to those of correctly inferring complex functions from limited samples of input-output pairs.
Finally, there are many services that require complex computations on the part of the provider, such as obtaining food and providing it to others, but that we do not conceptualize as computational because the primary benefit to the recipients is not informational or improved decision-making. In short, we restrict the concept of computational services to computations that could, in principle, be performed by the recipient’s nervous system, like diagnosing illness, but are instead performed by the service provider’s nervous system.
Our paper is organized as follows. We first argue that the Plio-Pleistocene transition to a more carnivorous dietary niche in open habitats intensified selection in the human lineage for cooperation in larger groups and for pathogen defense. We present shamans, a type of healer common in hunter-gatherer societies, as an important example of a computational service provider whose roles might have arisen in response to intensified pathogen pressure. We then conceptualize knowledge-based leaders as decision-making and conflict resolution specialists, computational services that might have arisen in response to intensified selection for cooperation in larger groups. Both roles might have been subject to sexual selection for cognitive abilities, contributing to encephalization. Finally, we argue that healing and decision-making services might have initially been naturally selected in mothers, who provide numerous medical and computational services for their cognitively immature offspring who are not yet able to provide these services for themselves.
2 The transition to open terrestrial habitats and carnivory intensified selection for cooperation in larger groups and for pathogen defense
The Plio-Pleistocene transition of the human lineage from a partially arboreal, woodland niche with a plant-based diet (Almécija et al., 2021) to a committed terrestrial lifestyle in a more open habitat with a more carnivorous diet (Antón, Potts, & Aiello, 2014) likely increased selection for greater cooperation for predator defense (Willems & van Schaik, 2017), and for scavenging and hunting large herbivores (Domínguez-Rodrigo et al., 2021; Domínguez-Rodrigo & Pickering, 2017; Pobiner, 2020; Smith, Swanson, Reed, & Holekamp, 2012; Szilágyi, Kovács, Czárán, & Szathmáry, 2023). It also likely increased zoonotic spillover, increasing selection for physiological and behavioral immune responses to zoonotic pathogens (Hagen, Blackwell, Lightner, & Sullivan, 2023).
Although the timing for each is uncertain, the human lineage’s new Pleistocene niche also involved the evolution of many other important traits, such as biparental and alloparental care (Burkart, Hrdy, & Van Schaik, 2009), multilevel social organization (Hamilton, Walker, Buchanan, & Sandeford, 2020), sophisticated symbolic communication (language) (Pinker & Jackendoff, 2005), and cumulative culture (Muthukrishna, Doebeli, Chudek, & Henrich, 2018; Richerson & Boyd, 2005). All of these were probably interrelated causes and consequences of the tripling of brain size over the course of the Pleistocene (Figure 2) and the consequent increased energetic requirements of the human nervous system, whose fitness costs, we argue, could have been partially offset by offering computational services to others.
2.1 Predation increased selection for cooperation in larger groups
Plio-Pleistocene East African herbivore communities included numerous megaherbivores (>1000kg) and the large (>100kg) carnivores that preyed on them, such as giant hyenas, sabertooth cats, lions, and highly carnivorous bears (Faith, Rowan, & Du, 2019; Treves & Palmqvist, 2007). Many of these carnivores outweighed hominins, could outrun them, and hunted in packs (Figure 3).
Primate species inhabiting open terrestrial habitats live in larger groups than those inhabiting wooded arboreal habitats, have more males in the group and greater sexual dimorphism, and the males frequently cooperate in counter-attacks against terrestrial carnivores. Chimpanzees and savanna baboons, two species that illustrate these patterns, often form groups with more than 100 individuals when far from the safety of trees, and the males engage in joint counter-attacks against large carnivores, occasionally using stones or sticks (Willems & van Schaik, 2017). The hominin transition to open, terrestrial habitats would therefore have been possible only with joint predator defense provided by a large group of highly cooperative males that probably used weapons of some sort (Bickerton & Szathmáry, 2011; DeVore & Washburn, 1963; Pobiner, 2020; Treves & Palmqvist, 2007; Van Valkenburgh, 2001; Willems & van Schaik, 2017).
Larger groups would have increased within-group competition for food, however (Alexander, 1974; Wheeler, Scarry, & Koenig, 2013), and also increased the risk of free-riders and other barriers to collective action (Powers & Lehmann, 2016; Powers, Schaik, & Lehmann, 2021), problems to which we will return.
2.2 Carnivory increased selection for cooperation in larger groups
The Pleistocene transition to a more carnivorous diet by Homo would also have increased its exposure to predators as it contested with them for carcasses. Lions and spotted hyenas, for instance, the two largest contemporary African carnivores, scavenge kills from each other and from smaller carnivores. Lions are responsible for 20-50% of spotted hyena deaths, probably to reduce competition rather than to provide nutrition, and they also regularly kill wild dogs, accounting for 30-50% of all deaths of pups and adults (Van Valkenburgh, 2001). Homo was therefore plausibly targeted as a competitor, further selecting for cooperative predator defense (e.g., Daujeard et al., 2016; cf. Speth, 2024).
The relative importance of animal vs. plant foods for early Homo, and whether it was hunted or scavenged, are hotly debated (Domínguez-Rodrigo & Pickering, 2017; Pobiner, 2020). Nevertheless, evidence for early access to large herbivore carcasses, including those of megaherbivores, c. 1.8 million years ago with the appearance of Homo erectus and the transition from Oldowan to Acheulean stone tools, suggests that cooperative hunting was now part of the behavioral repertoire of the human lineage (Domínguez-Rodrigo et al., 2021; Domínguez-Rodrigo & Pickering, 2017), another selection pressure for cooperation in larger groups. Plants remained an important and more reliable food source, however (Crittenden & Schnorr, 2017). The necessity to pool men’s high variance big game hunting returns, combined with women’s low variance gathering returns, are pillars of the cooperative sexual division of labor that characterizes most ethnographically known hunter-gatherer societies (Kelly, 2013).
Finally, cooperative territorial defense is common in social carnivores (Smith et al., 2012), and well-documented in chimpanzees, a highly territorial species that cooperatively patrols and defends boundaries with hostile and sometimes lethal interactions between groups (Mitani & Watts, 2005). Bonobos, though much more tolerant, nevertheless distinguish ingroup from outgroup members and occasionally exhibit hostility to outgroups (Langergraber, Watts, Vigilant, & Mitani, 2017; Samuni, Langergraber, & Surbeck, 2022). Some baboons and other primates and mammals live in multilevel societies, a relatively rare form of social organization, and engage in group-level cooperation against intruders (Grueter, Matsuda, Zhang, & Zinner, 2012; Grueter et al., 2020). Modern humans also live in multilevel societies with cooperative and competitive relationships among groups (Dyble et al., 2016; Hamilton et al., 2020; Pisor & Surbeck, 2019; Rodseth, Wrangham, Harrigan, & Smuts, 1991). In ethnographically known foraging societies, territoriality ranges from essentially non-existent to cooperative physical defense of clearly defined boundaries (Codding, Parker, & Jones, 2019; Moritz, Scaggs, Shapiro, & Hinkelman, 2020). It is, therefore, a reasonable supposition that groups of early Homo, and maybe earlier hominins, might also have cooperated to defend their territories, perhaps in larger multilevel societies.
The upshot is that the transition to a committed terrestrial lifestyle in open habitats, coupled with increased scavenging and hunting of large herbivores and perhaps cooperative defense of larger territories, increased selection for cooperation within and between large groups in the human lineage, requiring some solution to the increased within- and between-group competition and conflict that would inevitably arise. We will propose that knowledge-based leaders emerged to help solve these problems using exceptional computational resources.
2.3 Carnivory increased selection for pathogen defense
The transition from the plant-based diets of Australopithecines and other early hominins to greater meat-eating c. 2.6 million years in genus Homo ago likely increased zoonotic pathogen pressure (Hagen et al., 2023). Although plant foods are often contaminated with animal pathogens, e.g., in feces, the threat from plant pathogens themselves is relatively low due to the substantial differences between plant and animal cell walls and immune systems (Kim, Yoon, Park, Kim, & Ryu, 2020). Meat, on the other hand, would often have been infected with pathogens adapted to primates and other mammals that had a high risk of spillover into hominins. Most human infectious diseases indeed originate in non-human animals, hunting is associated with spillover into modern humans, and hunter-gatherers, bushmeat hunters, and veterinarians have increased zoonotic infections relative to others living in the same environments. Hunters and scavengers in the genus Homo would have had intimate, near-daily contact with mammalian prey and predators, and their pathogens and arthropod disease vectors. Some carnivory-related Plio-Pleistocene pathogen spillovers, including a tapeworm that can infect the brain, are still with us today (Hagen et al., 2023).
Pathogens are consistently found to be a primary selection pressure in humans (Uricchio, Petrov, & Enard, 2019), with helminths, one of the most common classes of zoonotic pathogens (Peros, Dasgupta, Kumar, & Johnson, 2021), exerting a particularly strong effect (Fumagalli et al., 2011). Consistent with increased zoonotic pathogen pressure, the human lineage evolved a number of defenses that diverged from chimpanzees and other primates. These include: exceptionally low stomach pH compared to other primates, a pathogen defense that is closely related to carnivory; a loss-of-function mutation in the CMAH gene that arose c. 2 mya in the human lineage, radically changing cell surfaces, the point of entrée for pathogens, and triggering subsequent evolution in immune-related Siglec genes that exceeds that seen in other apes; exceptional human immune responses to lipopolysaccharide compared to other primates, suggesting greater costs of bacterial infections since divergence from chimpanzees; human-specific down-regulation of the ANTXR2 gene which would protect against increased exposure to zoonotic anthrax; and divergent APOE, which is linked, among other things, to meat-eating and pathogen exposure. These all point to a shift, and perhaps an intensification, in the pathogen environment of Homo compared to earlier hominins and other apes and primates (Hagen et al., 2023).
We propose that selection intensified in Homo for the plant-based self-medication strategies already in place in apes and other primates (Huffman, 2003) for two major reasons. The first was the carnivory-related shift and perhaps increase in zoonotic pathogen pressure. The second was the challenge of defending a large body and brain from pathogens across what would eventually become one of the longest lifespans of any mammal (Hagen et al., 2023). We will argue that shamans and other healers arose as one solution to these challenges.
2.4 Increased pathogen pressure selected for increased reliance on plant-based self-medication
Plants are attacked by the same broad classes of pathogens and parasites that attack humans and other animals–viruses, bacteria, protozoa, fungi, helminths, and arthropods. In response, the plant kingdom has evolved a broad array of defenses, including toxins. Plants produce an estimated \(10^5-10^6\) chemically unique structures, with 5000-15,000 structures per species, most of which comprise lineage-specific compounds involved in defense against plant consumers (Li & Gaquerel, 2021).
Plant defensive chemicals typically target protein functions in pathogens and herbivores. Functional groups, such as aldehydes and epoxides, can covalently bond to proteins, and phenolics can form hydrogen and ionic bonds, all of which disrupt protein functions. Most terpenoids are lipophilic, readily interacting with pathogen biomembranes, which often causes cell death. Alkaloids, a large and diverse group of nitrogen-containing compounds produced by a wide range of plant species, often target animal neural receptors or other steps in neural signaling (Wink, 2015).
There is increasing evidence that non-human animals have evolved to co-opt plant toxins to combat their own infections, a phenomenon termed self-medication (Boppré, 1984; de Roode, Lefevre, & Hunter, 2013; Huffman, 1997, 2017; Neco, Abelson, Brown, Natterson-Horowitz, & Blumstein, 2019; Rodríguez & Wrangham, 1993; Villalba & Provenza, 2007; Wrangham & Nishida, 1983; Yoshimura, Hirata, & Kinoshita, 2021). Self-medication has been reported in 71 mammalian species, including 46 primate species and 10 carnivore species. It involves, e.g., ingestion of whole leaves to expel parasites from the digestive system (mostly apes and elephants), rubbing fur with toxic plants (non-human primates), placement of bay foliage around the nest to reduce ectoparasites (rodents), and use of specific plants to attenuate negative effects of food ingestion (artiodactyls). Self-medication evolved independently at least four times and is associated with greater body size, brain size, and longevity (Neco et al., 2019), traits that increased in the human lineage in the Pleistocene. There is also evidence for medicinal plant use by Neanderthals (Hardy, Buckley, & Huffman, 2013).
Thornhill & Fincher (2014) proposed that pathogen pressure increased selection for human behavioral immunity. We similarly propose that the human lineage, entering a niche that increased exposure to zoonotic pathogens, began to evolve cognitive mechanisms to more effectively utilize plant toxins to fight pathogens1. Due to the high cost of Western medicine, the majority of the world’s population still relies on plant-based traditional medicine (Hagen et al., 2023).
3 Shamans and other healers as computational service providers
Intensified use of plant-based medicine likely required an increased cognitive ability to assess ambiguous symptoms in individuals of varying ages, sexes, exposures, and circumstances, to classify distinct illness conditions, to discover which plant substances were the most effective, and then to store and recall the solutions (memory). To illustrate the complexity of this task with a simple example, there are 175 unique combinations of 1-3 symptoms out of 10, i.e., up to 175 distinct illnesses, and 210 unique combinations of 1 or 2 substances out of 20, i.e., up to 210 treatments. To determine which combinations of local plant substances best treated which illnesses it would be necessary to sift through 175 x 210 = 36,750 matches of possible treatments to illnesses. Such an exhaustive search is intractable (Arle & Carlson, 2020). We are not proposing that humans evolved to test every combination of substances against every combination of symptoms, however, and remember each outcome. We are proposing that making good use of the local and continually evolving “pharmacy” of plant compounds against continually evolving pathogens, using both individual and social learning to enable cumulative cultural evolution, would have required substantial computational resources (processing and memory).
Ethnoscience and ethnomedicine refer to culturally varying, locally useful bodies of conceptual knowledge about the social and natural world (Lightner, Heckelsmiller, & Hagen, 2021b) and illness and health (Quinlan, 2011), respectively. We distinguish products of knowledge, which refers to observable applications of knowledge, from know-how, which refers to the underlying cognitive system or process that reliably yields a desired product. Importantly, although some types of know-how, such as food preparation or tool use, can be reliably inferred from its products (e.g., observing the butchering of an animal), others cannot. A doctor knows how to diagnose and treat illnesses, for example, but her patients do not gain this know-how by observing the doctor (Lightner et al., 2021b).
A study of ethnoscientific expertise in ethnographic records from 55 traditional cultures found that although there were many domains of expertise, medicine was by far the most common (Lightner et al., 2021b). See Figure 4. This study also found two basic types of expertise. One involved easily-observed motor-based skills, such as woodworking and crafts, that are important for subsistence and other tasks performed by most community members on a daily basis. Experts in these domains had prestige and taught others, corresponding to influential theoretical models of prestige-biased cultural transmission (Henrich & Gil-White, 2001). The other type of expertise, our concern here, involved providing solutions to uncommon but serious problems, such as illness. Knowledge in these domains, primarily medicine and divination, was typically restricted and proprietary. Experts, who competed for clients based on a reputation for efficacy, provided their medical and other services in exchange for some type of “payment”, which we refer to as market for specialists (Lightner et al., 2021b, 2021a).
This pattern could be explained as follows. Under our hypothesis, although increased carnivory led to increased zoonotic spillover and use of pharmacological plant substances in the population of early humans as a whole, individual infection by a large number of different zoonotic pathogens would have been rare. Furthermore, many zoonotic diseases, such as anthrax and rabies, do not transmit from human to human. As a consequence, it might not have been worthwhile for all individuals to invest in acquiring the extensive medical knowledge needed for self-diagnosing and treating numerous illnesses that they might never acquire. But it could have been worthwhile for a few individuals to make a heavy investment and then cultivate a large clientele that would “pay” for their services when needed (Hagen et al., 2023; Lightner et al., 2021b, 2021a), a dynamic that could account for the appearance of ethnomedical experts that are commonly referred to as shamans or healers.
3.1 Shamans
In hunter-gatherer societies, which provide insights into the conditions under which humans evolved, healing services are typically provided by shamans. A study of a global sample of hunter-gatherer societies found that 79% had shamans, defined as a socially recognized part-time ritual intercessor, healer, and problem solver (Peoples, Duda, & Marlowe, 2016). Several societies categorized in this study as lacking shamans in fact have them, however, for a total of 88% (Singh, 2018). There is also archaeological evidence for shamanism in prehistory, including in paleolithic foragers (Lewis-Williams, 2001; Price, 2001). Conversely, shamans in all societies provide healing services (Singh, 2018), hence our focus on them here.
The word shaman comes from the Tungus language group, spoken by, among others, the Evenk, nomadic Siberian reindeer herders (Harvey & Wallis, 2007). The etymology of the term has been debated for more than a century. A recent treatment concludes that the root word, sar, means knowing or understanding, and shaman means a wise man who knows everything (Guo & Liang, 2015).
The Christian Europeans who first encountered and reported on Evenki shamans in the 17th century (e.g., Figure 5), observing them jump, dance, throw themselves on the ground, and call “demons” to divine the future (e.g., Petrovich, 2001), construed them to be specialists in the shamanism faith, a religious framing that continues to dominate academic studies of shamanism (e.g., DuBois, 2009; Eliade, 1964; Lewis, 2002; Winkelman, 2021a). These early encounters were followed by centuries of suppression and persecution of shamans by Christian (and later, Soviet) officials (Harvey & Wallis, 2007; Vitebsky & Alekseyev, 2015).
